In mobile radio, an electromagnetic wave is applied to the antenna of the mobile radio receiver. The electromagnetic wave contains a radio-frequency signal which has been modulated by convolutional-coded information and parity bits. The antenna converts the electromagnetic wave to electrical signals. The electrical signals are converted to baseband by multiplication by coherent carriers. The radio-frequency signal is filtered out of two or more channels by means of bandpass filtering, and is selected from the time-division multiplex by time windowing. Furthermore, the radio-frequency signal is digitized during the described conversion process. An equalizer is used to estimate the transmitted channel-coded data bits bk from the received signal, and these are then decoded in a channel decoder. The equalizer and channel decoder are used to calculate out signal distortion and faulty bits which are produced in the transmitted signal during the transmission by a channel, as well as punctured bits, out of the received signal. The known Viterbi algorithm provides a powerful and widely used calculation method, which can be used both for equalization and for channel decoding.
Particularly with regard to equalization, it has been found to be advantageous to estimate error probabilities pk in addition to the equalized data bits bk. Each data bit bk has an associated error probability pk. The channel decoding which is carried out after the equalization process is considerably improved when using the error probabilities pk.
The error probabilities pk have to be temporarily stored if they are intended to be used once again for incremental redundancy purposes in the GSM EGPRS (Enhanced General Packet Radio Service). In this case, incorrectly received data bits bk are transmitted once again with the same or different puncturing, thus increasing the redundancy.
The error probabilities pk are stored in the form of so-called soft output values sk. The soft output values sk can be calculated from the error probabilities pk as follows:
                              s          k                =                  1          ⁢                      n            ⁡                          (                                                1                                      p                    k                                                  -                1                            )                                                          (        1        )            
In the situation where the equalizer cannot make any statements about the received data bit bk, the error probability pk is equal to 0.5, and the associated soft output value sk is equal to 0. In the situation where the transmission conditions are good and, in consequence, the error probability pk assumes the value 0, the associated soft output value sk is infinitely large or, in an actual digital system, reaches a saturation value. In the situation where the error probability pk is greater than 0.5, the associated soft output value sk becomes negative. In this situation, it is more probable that the data bit 1−bk has been transmitted, rather than the data bit bk. However, this situation will not be considered in the following text.
The equalizer cannot make any statement about data bits bk which have been punctured after the convolutional coding. Before the channel decoding process, these data bits bk are each assigned a soft output value sk of 0, so that they have no significance for the channel decoder.
Channel-coded data bits bk are decoded together with their soft output values sk in the channel decoder. Without the soft output values sk, the performance of the channel decoder would be greatly restricted. The decoded data bits contain a checksum word, which can be used by a consistency test to determine whether the transmitted and deconvolved data bits are error-free. If the data bits are found to be free of errors, the data block is passed on. Otherwise, a change is made to a lower-order modulation type and channel coding (MCS; Modulation and Coding Scheme), or the data block is retransmitted. The new request for the data block can also be repeated two or more times. The data bits bk which have been received two or more times and their soft output values sk are combined in a suitable manner, and are deconvolved jointly.
The memory requirement for storage of the data bits bk and of their soft output values sk is not negligible in integrated circuits. A depunctured data block comprises 1836 bits. By way of example, the memory requirement for four data blocks has approximately 60 kBits, if the number Nsoft of bits in a soft output value sk is 7. It is thus desirable to store the soft output values sk as integer soft output values sD,k with as short a data length as possible. In order to store the theoretically possible range from 0 to infinity as soft output values sD,k, the soft output values sk are quantized and saturated.
However, both excessively coarse quantization and saturation quickly have a negative effect on an excessively small value range. In neither case can the charnel decoder reasonably assess the quantized soft output values. If the transmission conditions are very poor, excessively coarse quantization will in some circumstances result in soft output values sD,k equal to 0 being applied to a large number of data bits bk, and in them subsequently being rejected. If the transmission conditions are very good, saturation leads to an excessively narrow value range in which a large number, or even all, of the soft output values sD,k are greater than the maximum value 2Nsoft−1, so that it is no longer possible to distinguish between the error probabilities.
For optimum utilization of the numerical range predetermined by the number Nsoft, it is known for the soft output values sk to be scaled by a scaling factor c before quantization, that is to say for the soft output values sk to be divided by the scaling factor c. In the GSM service EGPRS, the value range of the soft output values sk, which is otherwise wide for this service, is linearly compressed. However, this results in an increase in the quantization errors.
Non-linear compression may also be used instead of linear compression. The quantization error can thus advantageously be redistributed. By way of example, the non-linear compression can be carried out in such a way that the quantization error decreases for relatively small soft output values, and increases for large soft output values. However, non-linear compression has the disadvantage that this results in the input variable to the channel decoder being subjected to non-linear distortion.